Organically ranked knowledge categorization in a knowledge management system

ABSTRACT

Embodiments of the present invention address deficiencies of the art in respect to expert modeling in a KM system and provide method, system and computer program product for organically ranked knowledge and categorization for a KM system. In one embodiment of the invention, a method for organically ranked knowledge and categorization in a KM system can be provided. The method can include bookmarking answer content for a first end user of the knowledge management system, suggesting a set of categories previously associated with the answer content by other end users of the knowledge management system, and categorizing the bookmarked answer content with a category selected from the set of categories.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation of U.S. application Ser. No.13/722,202, filed on Dec. 20, 2012, now allowed, which is a Divisionalof U.S. application Ser. No. 13/323,777, filed on Dec. 12, 2011, nowU.S. Pat. No. 8,341,107, which is a Divisional of U.S. application Ser.No. 11/761,776, filed on Jun. 12, 2007, now U.S. Pat. No. 8,078,565,which are incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the field of customer relationshipmanagement and more particularly to automated inquiry resolution forcustomer relationship management systems.

2. Description of the Related Art

The corporate enterprise faces a difficult challenge when attempting tosimultaneously improve the quality of customer service while reducingservice costs. More products, growing product complexity, third partyoriginal equipment manufacturer components, and rapid changesubstantially increase the amount of information required to answercustomer questions and troubleshoot problems. Paradoxically, thisinformation overload has produced an information famine in which thegrowth of information availability increases the difficulty of findingrelevant information—particularly in an online, automated computingenvironment.

For the corporate enterprise to improve self-service adoption rates,increase call center efficiency and improve response accuracy, solutionsare required that assist each of agents, customers, partners andsuppliers in finding answers to questions more efficiently. As a result,effective solutions to information search and retrieval have becomecritical to inquiry resolution. One popular approach includes deployinga search engine that allows users to sift through many informationsources. Typically, search engines offer any or a combination of akeyword, simple text and natural language query interface.

While the utilization of a search engine for self-service informationretrieval for inquiry resolution has become commonly understood, thisapproach has demonstrated significant limitations. In particular, thesearch engine is best suited for use by expert users who are familiarwith the content and terminology being searched and who know whichsearch words will most quickly yield a correct answer. However, userswithout domain expertise cannot easily apply the precision and relevancerequired for efficient retrieval. Most will recall the experience ofentering a few keywords into a search engine only to receive a resultingset of hits numbering in the thousands.

To address the limitations of the basic search engine for informationretrieval, the corporate enterprise has turned to the knowledgemanagement (KM) system to better manage and share information. The KMsystem has been defined as an “IT (Information Technology)-based systemdeveloped to support and enhance the organizational processes ofknowledge creation, storage/retrieval, transfer, and application.” TheKM system intends to enable users to access to knowledge of facts,sources of information, and solutions of an organization in the courseof inquiry resolution.

The modern KM system often takes the form of a document based systemutilizing technologies that permit the creation, management and sharingof formatted documents. Advanced forms of the KM system include ontologybased systems incorporating terminologies used to summarize a document,and artificial intelligence (AI) technologies utilizing a customizedrepresentation scheme to represent a problem domain. Generally, in amodern KM system, for inquiry resolution one or more answering serversprocess answer client requests for solutions statically with returnedcontent, or actively with the conduct of a transaction.

The modern KM system provides a knowledgebase of articles answeringquestions posed by inquiring users. The inquiring users generally notonly include customers, but also include customer servicerepresentatives seeking answers to customer questions. Inquiring usersarrive at the desired article either by direct search engine query,through case based reasoning, or through AI based expert modeling inwhich a sub-set of selected articles are presented by reference to theinquiring user as a best guess of the desired articles.

The latter mechanism can be quite complex, however, in that in many KMsystem implementations an intensive manual process can be undertaken atgreat expense to provide the data necessary to enable the AI mechanism.In particular, the manual process often involves the collection of thestatistical preferences of domain experts to produce a set of metricsranking each article relative to a posed question. Consequently, thehighest ranking articles are presented in a list to an inquiring user inresponse to the posed question. Due to the cost of enabling the AImechanism, the AI mechanism has been omitted from many a KM system.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention address deficiencies of the art inrespect to expert modeling in a KM system and provide method, system andcomputer program product for organically ranked knowledge andcategorization for a KM system. In one embodiment of the invention, amethod for organically ranked knowledge and categorization in a KMsystem can be provided. The method can include bookmarking answercontent for a first end user of the knowledge management system,suggesting a set of categories previously associated with the answercontent by other end users of the knowledge management system, andcategorizing the bookmarked answer content with a category selected fromthe set of categories.

In this regard, in an aspect of the embodiment, suggesting a set ofcategories previously associated with the answer content by other endusers of the knowledge management system can include sorting a set ofcategories previously associated with the answer content by other endusers of the knowledge system according to a rank order determined byself-learning scores for each of the categories, and presenting thesorted set of categories to the first end user. Additionally, the methodcan include adjusting the self-learning scores for the categories toaccount for the selected category. Yet further, the method can includeweighting different ones of the self-learning scores to account forcategory selections by expert end users of the knowledge managementsystem.

In another aspect of the embodiment, uncategorized answer content can becompared to already categorized content to identify similar answercontent. Thereafter, uncategorized answer content can be categorizedwith categories already associated with similar categorized answercontent. Finally, in yet another aspect of the embodiment, the methodeven yet further can include bookmarking additional answer content forthe first end user already categorized with the selected category by theother end users of the knowledge management system. In this way, answercontent most likely to be relevant to the end user can be providedproactively to the end user without requiring intervention on behalf ofthe end user.

In another embodiment of the invention, a KM data processing system canbe provided. The system can include a data store of categorizationsincluding records associating answer content in the knowledge managementdata processing system with end user applied categories. The system alsocan include an expert modeler coupled to the data store and configuredto apply a ranked order to sub-sets of categories in the data store ofcategorizations according to self-learning scores applied to thecategories. Finally, the system can include organic ranked knowledgecategorization logic. The logic can include program code enabled tosuggest a set of categories previously associated with end userbookmarked answer content by other end users of the knowledge managementsystem and to categorize the bookmarked answer content with a categoryselected from the set of categories.

Additional aspects of the invention will be set forth in part in thedescription which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. The aspectsof the invention will be realized and attained by means of the elementsand combinations particularly pointed out in the appended claims. It isto be understood that both the foregoing general description and thefollowing detailed description are exemplary and explanatory only andare not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute partof this specification, illustrate embodiments of the invention andtogether with the description, serve to explain the principles of theinvention. The embodiments illustrated herein are presently preferred,it being understood, however, that the invention is not limited to theprecise arrangements and instrumentalities shown, wherein:

FIG. 1 is a pictorial illustration of a KM system configured fororganically ranked knowledge and categorization;

FIG. 2 is a schematic illustration of a KM system configured fororganically ranked knowledge and categorization for a KM system; and,

FIG. 3 is a flow chart illustrating a process for organically rankedknowledge and categorization in the KM system of FIG. 2.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present invention provide a method, system andcomputer program product for organically ranked knowledge andcategorization in a KM system. In accordance with an embodiment of thepresent invention, prior categorizations of an answer object can bepresented to a question posing client seeking to categorize the answerobject. The ordering of the categorizations can derive from a frequencyof categorization for the answer object for the prior categorizations.Thereafter, the categorized answer objects can form a basis forautomatic categorization of similar, non-categorized answer objectswhere the categorized answer objects serving as positive categoryexamples.

In further illustration, FIG. 1 is a pictorial illustration of a KMsystem configured for organically ranked knowledge and categorization.As shown in FIG. 1, a KM system 110 can be accessed by a querying user120 seeking knowledge in response to a posed question. In response tothe posed question, the KM system 110 can provide a set of answercontent, for example articles, in consequence of which the querying user120 can select particular answer content 130 of interest. To the extentthat the querying user 120 desires to recall the selected answer content130 at a later time, the querying user 120 can “bookmark” the selectedanswer content 130 by adding the selected answer content to thefavorites 160 for the querying user 120.

Upon the querying user 120 adding the selected answer content 130 to thefavorites 160, the KM system 110 can prompt the querying user 120 tocategorize the selected answer content 130. In this regard, a sortedlisting of selected categories 140 can be provided to the querying user120. The sorted listing of selected categories 140 can include a rankedordering of categories previously assigned to the selected answercontent by other users 150 of the KM system 110 and ranked according toself-learning scores applied to each of the categories. The queryinguser 120 can select a category in the sorted listing of selectedcategories 140, or the querying user 120 can apply a new category. Ineither circumstance, the self-learning scores for the categories can beadjusted to account for the selection of the querying user 120.

Notably, the sorted listing of selected categories 140 can be applied toother answer content 170 automatically by comparing the other answercontent 170 to similar answer content already categorized according tothe sorted listing of selected categories 140. Additionally, thecategorizations applied by the other users 150 can be weighted accordingto the perceived expertise of different ones of the other users 150. Inthis regard, it will be recognized that some of the other users 150 willbe frequent contributors of answer content demonstrating a degree ofexpertise able to factored into the self-learning scores for thecategories of the KM system 110. Finally, once a category has been addedto the favorites 160 for the querying user 120, additional answercontent categorized according to the added category can be added to thebookmarks though unsolicited by the querying user 120 in order topromote the additional answer content as relevant to related queries.

In further illustration, FIG. 2 is a schematic illustration of a KMsystem configured for organically ranked knowledge and categorizationfor a KM system. The system can include an answering server 230 coupledto a KM system 240. The answering server 230 can be configured forcommunicative linkage to one or more client computing devices 210 over acomputer communications network 220. In this regard, each of the clientcomputing devices 210 can include a computing platform, for instance apersonal computer, personal digital assistant or computing enabledwireless phone through which querying users can post questions to the KMsystem 240 and receive answer content from the KM system 240.

The KM system 240 can be configured to categorize answer contentprovided by the KM system 240 in response to user provided questions.The categories can be associated with answer content in a data store ofcategorizations 260. The data store of categorizations 260 can include aset of records specifying different answer content and correspondinglylinked categories. Of note, the answer content in the data store ofcategorizations 260 can be linked to different categories by differentend users such that observable metrics can be computed for the answercontent and for the categories including a frequency of association ofdifferent categories to different answer content and a number ofassociations established between different categories and answercontent. Using the observable metrics, contributing end users can beranked—those end users having answer content more frequently linkedhaving a higher ranking while those end users having answer contentoften found to be obsolete having a lower ranking

Consequently, expert modeling logic 250 coupled to the KM system 240 andto the data store of categorizations 260 can process the observablemetrics for selected instances of answer content to provide a rankordering of categories. In this regard, organic ranked knowledgecategorization logic 300 can be coupled to the KM system 240 and caninclude program code enabled to provide the rank ordering of categoriesto an end user in response to the end user bookmarking answer content270 received from the KM system 240. The end user, in turn, can selectone of the categories in the rank ordering to be applied to thebookmarked answer content 270, or the end user can create a new categoryfor the bookmarked answer content 270. In either circumstance, theexpert modeling logic 250 can adjust the observable metrics to accountfor the end user categorization of the bookmarked answer content 270.

Notably, the program code of the organic ranked knowledgecharacterization 300 can be further enabled to process uncategorizedanswer content by assigning categories to uncategorized answer contentcorresponding to categories already assigned to comparable answercontent. In particular, uncategorized answer content can be compared toalready categorized answer content and when uncategorized answer contentis determined to be similar enough to categorized answer content, apredominant category assigned to the categorized answer content likewisecan be assigned to the similar, uncategorized answer content. In thisway, structure can be established for amorphous knowledge in a knowledgebase leveraging the subjective structuring preferences of the users ofthe knowledge base.

In yet further illustration of the operation of the organic rankedknowledge categorization logic 300, FIG. 3 is a flow chart illustratinga process for organically ranked knowledge and categorization in the KMsystem of FIG. 2. Beginning in block 310, answer content can be servedto an end user of the KM system. In block 320, the bookmarking of theanswer content can be detected and in block 330, a set of categories canbe retrieved for the answer content as previously applied by other usersof the KM system. Thereafter, in block 340 the categories in the set canbe sorted by rank and the end user can be prompted to select one of thecategories in the set in block 350.

In block 360, a selection of a category by the end user can bedetermined. In decision block 370, it can be further determined whetherthe end user selected a category from amongst the rank orderedcategories in the set, or whether the end user selected a new category.In the former circumstance, in block 380 the selected category can beadded locally to the bookmarks of the end user and in block 400 thebookmark can be categorized as such. Optionally, answer contentpreviously categorized by other end users under the selected categorycan be proactively bookmarked locally for the end user. In the lattercircumstance, however, a new category can be created and locally to thebookmarks in block 390 and again the bookmark can be categorized as suchin block 400. Finally, in block 410 the learning scores for thecategories can be adjusted to reflect the end user selection.

In an optional embodiment, the movement of answer content by differentend users from one category to another can be monitored. When enough endusers have re-categorized answer content from one category to another,the end users who have linked to the category can be notified of theimpending obsolescence of the category. In this way, end users canremain abreast of knowledge trends.

Embodiments of the invention can take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment containingboth hardware and software elements. In a preferred embodiment, theinvention is implemented in software, which includes but is not limitedto firmware, resident software, microcode, and the like. Furthermore,the invention can take the form of a computer program product accessiblefrom a computer-usable or computer-readable medium providing programcode for use by or in connection with a computer or any instructionexecution system.

For the purposes of this description, a computer-usable or computerreadable medium can be any apparatus that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution. Input/output or I/Odevices (including but not limited to keyboards, displays, pointingdevices, etc.) can be coupled to the system either directly or throughintervening I/O controllers. Network adapters may also be coupled to thesystem to enable the data processing system to become coupled to otherdata processing systems or remote printers or storage devices throughintervening private or public networks. Modems, cable modem and Ethernetcards are just a few of the currently available types of networkadapters.

1. In a knowledge management system; a method for organically rankedknowledge and categorization, the method comprising: bookmarking answercontent by adding the answer content to favorites for a first end userof the knowledge management system executing in memory of a computer;suggesting a set of categories previously associated with the answercontent by other end users of the knowledge management system inresponse to the first end user bookmarking answer content; determiningwhether the first end user selected a category from the suggested set ofcategories or whether the first end user selected a new category; upondetermining the first end user selected a new category, creating the newcategory and also categorizing the bookmarked answer content accordingto the new category; and, upon determining the first end user selectedthe category from the suggested set of categories, categorizing thebookmarked answer content with a category selected from the set ofcategories.
 2. The method of claim 1, wherein suggesting the set ofcategories previously associated with the answer content by other endusers of the knowledge management system in response to the first enduser bookmarking answer content, comprises: sorting the set ofcategories previously associated with the answer content by other endusers of the knowledge system according to a rank order determined byself-learning scores for each of the categories; and, presenting thesorted set of categories to the first end user.
 3. The method of claim2, further comprising adjusting the self-learning scores for thecategories to account for the selected category.
 4. The method of claim1, wherein suggesting the set of categories previously associated withthe answer content by other end users of the knowledge management systemin response to the first end user bookmarking answer content comprisesweighting different ones of the self-learning scores to account forcategory selections by expert end users of the knowledge managementsystem.
 5. The method of claim 1, further comprising: comparinguncategorized answer content to already categorized content to identifysimilar answer content; and, associating uncategorized answer contentwith categories already associated with similar categorized answercontent.
 6. The method of claim 1, further comprising bookmarkingadditional answer content for the first end user already categorizedwith the selected category by the other end users of the knowledgemanagement system.
 7. The method of claim 1, further comprising:monitoring re-categorization of answer content; and, notifying selectedones of the end users linked to categories from which a threshold amountof answer content has been re-categorized.
 8. A knowledge managementdata processing system comprising: a computer with at least oneprocessor and memory; a data store of categorizations coupled to thecomputer and comprising records associating answer content in theknowledge management data processing system with end user appliedcategories; an expert modeler coupled to the data store, the expertmodeler executing in the memory of the computer and configured to applya ranked order to sub-sets of categories in the data store ofcategorizations according to self-learning scores applied to thecategories; and, organic ranked knowledge categorization logiccomprising program code enabled when executed by the at least oneprocessor of the computer to detect the bookmarking of answer content,bookmarking comprising adding answer content to favorites of an enduser, to suggest a set of categories previously associated with end userbookmarked answer content by other end users of the knowledge managementsystem in response to the end user bookmarking answer content, todetermine whether the first end user selected a category from thesuggested set of categories or whether the first end user selected a newcategory, upon determining the first end user selected a new category,to create the new category and also categorizing the bookmarked answercontent according to the new category, and upon determining the firstend user selected the category from the suggested set of categories, tocategorize the bookmarked answer content with a category selected fromthe set of categories.
 9. The system of claim 8, wherein theself-learning scores are weighted to account for category selections byexpert ones of the other end users.
 10. The system of claim 8, whereinthe answer content are articles in the knowledge management system. 11.A knowledge management computer program product comprising anon-transitory computer usable medium embodying computer usable programcode for organically ranked knowledge and categorization, the computerprogram product comprising: computer usable program code for bookmarkinganswer content by adding the answer content to favorites for a first enduser of a knowledge management system; computer usable program code forsuggesting a set of categories previously associated with the answercontent by other end users of the knowledge management system inresponse to the first end user bookmarking answer content; computerusable program code for determining whether the first end user selecteda category from the suggested set of categories or whether the first enduser selected a new category; upon determining the first end userselected a new category, computer usable program code for creating thenew category and also categorizing the bookmarked answer contentaccording to the new category; and, upon determining the first end userselected the category from the suggested set of categories, computerusable program code for categorizing the bookmarked answer content witha category selected from the set of categories.
 12. The computer programproduct of claim 11, wherein the computer usable program code forsuggesting the set of categories previously associated with the answercontent by other end users of the knowledge management system inresponse to the first end user bookmarking answer content, comprises:computer usable program code for sorting the set of categoriespreviously associated with the answer content by other end users of theknowledge system according to a rank order determined by self-learningscores for each of the categories; and, computer usable program code forpresenting the sorted set of categories to the first end user.
 13. Thecomputer program product of claim 12, further comprising computer usableprogram code for adjusting the self-learning scores for the categoriesto account for the selected category.
 14. The computer program productof claim 12, wherein suggesting the set of categories previouslyassociated with the answer content by other end users of the knowledgemanagement system in response to the first end user bookmarking answercontent comprises computer usable program code for weighting differentones of the self-learning scores to account for category selections byexpert end users of the knowledge management system.
 15. The computerprogram product of claim 12, further comprising: computer usable programcode for comparing uncategorized answer content to already categorizedcontent to identify similar answer content; and, computer usable programcode for associating uncategorized answer content with categoriesalready associated with similar categorized answer content.
 16. Thecomputer program product of claim 11, further comprising computer usableprogram code for bookmarking additional answer content for the first enduser already categorized with the selected category by the other endusers of the knowledge management system.
 17. The computer programproduct of claim 11, further comprising: computer usable program codefor monitoring re-categorization of answer content; and, computer usableprogram code for notifying end users linked to categories from which athreshold amount of answer content has been re-categorized.